Project Summary/Abstract Atrial fibrillation (AF) is the most prevalent, major arrhythmia in the United States. It leads to an increased risk of stroke, congestive heart failure, and overall mortality. AF is also characterized by symptoms in a majority of patients that can result in significant decreases in health related quality of life and functional status, which are strong predictors of all-cause and cardiovascular hospitalizations in patients with AF. Therefore, improvement in symptoms is an important therapeutic goal in the management of patients with AF along with reducing the risk of stroke and mortality. However, previous studies evaluating symptoms in AF have been limited by their retrospective assessment of symptoms that limits our ability to assess the relationship between heart rhythm, symptoms, affect and functional status in real time. To address all of these gaps, we propose an innovative study that will intensively examine 100 patients with paroxysmal AF using a continuous heart rhythm recorder and a novel mobile application to collect data on symptom and affect ratings during multiple occasions across a day for three weeks. We will then be able to examine the relationship between symptoms, affect, heart rhythm as well as additional features within the ECG recording and assess their effect on functional status in patients with AF. We hypothesize that 1) some symptoms will be much more specifically indicative of being in AF (e.g. palpitations) than others (e.g. fatigue) 2) ECG features derived from signal processing and machine learning algorithms (especially those that serve as surrogates for autonomic function) will be more sensitive and specific for determining the presence and severity of symptoms compared to average heart rate 3) there will be a strong relationship between affect, and both symptoms and functional status . The overarching goal of this proposal is for candidate (Hamid Ghanbari, MD, MPH) to develop an independent research program examining symptoms and associated decline in functional status in patients with paroxysmal AF. The candidate will build upon his previous training by partnering with a team of mentors who are experts in ecological momentary assessment methodology, signal processing and machine learning, affect, and functional status to acquire expertise in evaluation of repeated, real-time assessments of symptoms and to explore novel ECG features that predict symptoms beyond the presence or absence of AF. In concert with the proposed study, the candidate will also pursue didactic training and one-on-one mentoring related to his research aims. This proposal will more clearly characterize symptoms and their physiological and psychological correlates and their subsequent influence on functional status in patients with AF. The insights obtained through this proposal could eventually lead to individualized behavioral and medical interventions that best address these symptoms and associated dysfunction.